The present invention generally relates to a method for attenuating stochastic effects in a digital image, and in particular, to a method for rejecting clutter in a digital image, such that structures in the digital image formerly obscured by such stochastic effects or clutter are more readily recognizable or detectable.
Various methodologies have been utilized over the years to achieve detection of structures or targets (e.g., man-made structures, such as roads, buildings, vehicles, etc.) from space and/or airborne platforms. One such methodology is known as automatic data screening, which is a critical capability for imagery collection systems operating in a wide area search mode. Such automatic data screening is particularly useful for timely processing image data, especially in tactical situation and in view of the diminishing number of imagery analysts available to exploit the collected imagery. However, automatic data screening is, at times, problematic due to difficulties in maintaining a tolerable false alarm rate (e.g., false detection of objects/targets). Other methodologies for detection of structures have involved local methods such as small kernel convolvers or morphological filters. Unfortunately, these particular methodologies can produce unexpected results. And finally, still other methodologies depend upon repetitive target structure or convolved clutter, which is not typically present.
Accordingly, it is an object of the present invention to provide a method for detecting objects of interest (e.g., man-made structures, such as vehicles, buildings, roads, etc.) from digital imagery.
It is another object of the present invention to provide a method for attenuating clutter or stochastic effects in digital imagery to enhance recognition or detection of objects of interest.
It is a further object of the present invention to provide a method for timely decorrelating stochastic effects in a digital image while preserving the structure(s) in the digital image, such that the structure(s) are recognizable (e.g., through conventional automatic target recognition methodologies) and a tolerable false alarm rate is maintained.
It is yet another object of the present invention to provide a method for rejecting clutter to enable detection/recognition of structures/objects/targets utilizing a single digital image (e.g., a digital image from an optical, infrared, or synthetic aperture radar imaging system).
The method of the present invention achieves one or more of these objectives by applying several (i.e., two or more) transforms to a single digital input image. Applying selected transforms, each of which is designed to reveal obscured objects of interest, to the single digital input image tends to preserve the structure(s) in the image while acting on the clutter (i.e., stochastic effects) in distinctly different ways, such that the clutter is decorrelated across the several transformed images. Such transformed images may be combined to produce a filtered image, whereby objects of interest are more readily detectable/recognizable than with any one of the transforms alone. Advantageously, only a single digital image of an area of interest is required by the method of the present invention. As such, the method of the present invention is particularly useful due to limited imaging resources, and the fact that multiple looks at the same area of interest may not be possible within a given required time frame.
Generally, the method of the present invention is particularly suited for filtering clutter from a first digital input image, such that at least a first object, target or structure in the first digital input image, formerly obscured by the clutter, is detectable/recognizable. In one aspect of the present invention, such filtering may be accomplished in a timely manner by applying or performing at least first and second transforms to first digital image data corresponding to the first digital input image, the first digital image data being in the image intensity domain, performing an appropriate filtering operation to retain, in first and second transform domains, at least a portion of transform indication(s) corresponding or at least correlatable to the structure, applying to the remaining transform indications inverse first and second transforms, and processing the filtered first digital image data to produce a single filtered image and/or image data.
More specifically, in one embodiment, the method of the present invention includes the steps of applying or performing a first transform to transform first digital image data corresponding to a first digital input image from one of an optical, infrared and synthetic aperture radar imaging system into first transformed digital image data in the first transform domain, applying or performing a second transform to transform the first digital image data into a second transform domain, the first and second transformed digital image data in the first and second transform domains each having first and second transform indications corresponding or at least correlatable to at least the first object and the clutter, respectively, filtering the transformed digital image data by retaining at least a portion of the first transform indications corresponding to the first object in the first and second transform domain, applying or performing an inverse first transform to transform at least a portion of the first transform indication in the first transform domain into at least a first filtered indication from the first transform domain, in the image intensity domain, and applying or performing an inverse second transform to transform at least a portion of the first transform indication in the second transform domain into at least a first filtered indication from the second transform domain, in the image intensity domain. In this embodiment, the first and second transform domains are different. For example, the first transform domain may be the Karhunen-Loeve or Eigen space domain and the second transform domain may be the wavelet coefficient domain. For purposes of further enhancing attenuation of clutter, such that the structure(s) of the image is/are detectable/recognizable, the method may further include the steps of applying or performing a third transform to transform the first digital image data into a third transform domain, the third transformed digital image data in the third transform domain having first and second transform indications corresponding or at least correlatable to at least the first object and clutter, respectively, filtering the third transformed digital image data to alter and/or modify the spatial frequency of the clutter/stochastic effects and to increase the contrast of the structure(s)/non-stochastic feature(s) relative to the clutter/stochastic effects in the third transform domain, and applying or performing an inverse third transform to transform the modified/altered third transformed digital image data into modified image data in the image intensity domain. The method may further include the step of combining two or more of the first filtered indications in the image intensity domain to produce a first filtered image, or, alternatively, the step of processing two or more of the first filtered indications in the image intensity domain using a principal component analysis, the result of which is that at least the first object may be recognizable or detectable now that the clutter or stochastic effects have been decorrelated.
In another aspect, the method of the present invention is directed to enhancing the recognizability/detectability of structures in a digital image in a timely manner, the structures being initially obscured by clutter or stochastic effects. In this aspect of the present invention, the method generally includes the step of prescreening first digital image data corresponding to a first digital image for candidate objects or structures of interest, such that at least a first digital sub-image may be selected, the first digital sub-image including at least a first candidate object of interest. Such prescreening is especially useful for analyzing wide area images for objects or structures in a timely manner, especially since such wide area images can encompass hundreds of square nautical miles. More specifically, in one embodiment of this aspect of the invention, the method includes the steps of prescreening first digital image data corresponding to a first digital image for at least a first candidate object to select a first digital sub-image, the first digital sub-image including at least the first candidate object and being a portion of the first digital image, applying or performing a first transform to the first digital sub-image data to transform the first digital sub-image data in the image intensity domain into first transformed sub-image data in a first transform domain, the first transformed data including at least first and second separable transform indications in the first transform domain corresponding or at least correlatable to at least the first candidate object and the stochastic effects, respectively, in the image intensity domain, retaining at least a portion of the first transform indication in the first transform domain, performing a first inverse transform to transform at least the first transform indication in the first transform domain into at least a first filtered indication of the first digital sub-image data from the first transform domain, which may result in at least the first candidate object being recognizable due to the decorrelation of the clutter. The steps of applying another transform different than the first transform to the first digital sub-image data, retaining at least a portion of a transform indication corresponding to the first candidate object and applying an inverse transform different than the first inverse transform to a retained transform indication may be conducted on the first digital sub-image data to further enhance recognizability/detectability of at least the first candidate object.
Generally, the step of prescreening the first digital image data functions to focus the recognition/detection analysis upon a selected portion of the first digital image. In one embodiment, the prescreening step includes the steps of performing a high pass filter transform to transform the first digital image data in the image intensity domain into transformed image data in a transform domain having at least first and second transforms separable indications in the transform domain corresponding to at least the first candidate object and the stochastic effects, respectively, in the image intensity domain, discarding at least a portion of the second transform indication in the transform domain, performing an inverse transform to transform at least the first transform indication in the transform domain into at least a first filtered indication of a first processed digital image data from the transform domain, and thresholding in the image intensity domain the first processed digital data from the transform domain for at least the first candidate object having at least a first image intensity level to select at least the first digital sub-image, the first digital sub-image including the first filtered indication from the transform domain, whereby the first candidate object corresponds to the first filtered indication from the transform domain. In one embodiment, the high pass filter transform domain is the Karhunen Loeve domain (i.e., Eigen space domain).
As such, the method of the present invention is particularly useful in detecting, recognizing and/or classifying a wide variety of obscured objects/targets/structures using imagery from various types of digital imagery (e.g., optical, infrared and synthetic aperture radar images). In addition, synergistic effects among various types of imaging systems could be exploited. For example, the method of the present invention could be applied to X-band synthetic aperture radar imagery to produce more information about obscured targets/objects/structures which were detected by an ultra high frequency radar system used to cue the X-band synthetic aperture radar system.